The opportunities offered by telematics development and the features required by travellers moving on more complex multimodal transit networks push to investigate new methods for generating path advice in transit trip planners. First, the study focuses on characteristics and limits of the methods used by current trip planners for path generation and then analyses the new methods applied by a new generation of trip planners (most at the prototypical developing stage). These methods use a group or, better, an individual traveller utility function, which allows personal preferences to be pointed out. As the individual utility functions in the transport modelling literature has been largely neglected, the second part of the study reports the state-of-the-art and some theoretical considerations on individual path utility function modelling, and recalls the approaches developed for including individual utility in new trip planners. Further, the results of individual utility function estimations, using surveys relative to the city of Rome, are presented and some aspects of the utility function calibration, including the info provider learning process of traveller preferences, are explored. The analyses show a significant improvement in using individual functions for path advice and a substantial influence of network complexity on the learning process.
Individual utility-based path suggestions in transit trip planners
IET Intelligent Transport Systems ; 10 , 4 ; 219-226
2016-05-01
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
complex multimodal transit network , telematics development , personal preference , transit trip planner , travel industry , path generation , user tracing , prototypical developing stage , intelligent transportation systems , traveller preference , learning (artificial intelligence) , planning (artificial intelligence) , transport modelling literature , utility function specification , path advice , group utility path function , utility-based path suggestion , info provider learning process , path choice , parameter estimation , path utility function estimation , current trip planner , individual traveller utility function , path utility function modelling
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